![]() In Tableau Prep Builder, this method is case insensitive and only applies to numbers and letters. It tokenizes the value into a character set and sorts the characters to generate a key, known as a 1-gram. Ĭommon Characters: This method is useful to fix capitalization or formatting issues. The two keys produce two groups of data values: For example, the values on the left are associated with the keys on the right. It uses the Metaphone3 algorithm to generate keys based on the value’s English pronunciation. Pronunciation: This method is useful for fixing data entry errors where words sound similar. In key-based methods like Pronunciation and Common Characters, each value is transformed to a key, or token, and all values with the same key are grouped together. Wouldn't it be great if a data preparation tool could help automate this task? Updating this script is still tedious as he works backwards from errors in his analysis. After spending a lot of time manually fixing the city names, he converted that work to a Python script as he found he has to repeat the standardization with every campaign. ![]() He finds that users misspell several cities, which leads to errors in his analysis as data is not correctly reported. John, a Tableau customer, analyzes marketing call data where agents manually enter responses across the US. To correctly analyze this data, users must manually reconcile data values, which can be error-prone and time-consuming. For example, a city field with “Seattle” spelled as “Seattel” an address field with two variations of 5th street as "5th St" and "St, 5th" or a customer name represented as "First name Last name" and "Last name, First name". Text fields in data tables often have data with misspelled values or multiple alternatives of the same concept.
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